Oral presentations and abstracts

Over the past decade there has been a large increase in the number of automated camera networks that monitor the sky for fireballs. One of the goals of these networks is to provide the necessary information for linking meteorites to their pre-impact, heliocentric orbits and ultimately to their source regions in the solar system. We re-computed heliocentric orbits for the 25 meteorite falls published in or before 2016 from original data sources (Granvik and Brown 2018). Using these orbits, we constrained their most likely escape routes from the main asteroid belt and the cometary region by utilizing a state-of-the-art orbit model of the near-Earth-object population (Granvik et al. 2016), which includes a size-dependence in delivery efficiency. While we find that the general results for escape routes are comparable to previous work, the role of trajectory measurement uncertainty in escape-route identification is explored for the first time. Moreover, the improved size-dependent delivery model substantially changes likely escape routes for several meteorite falls, most notably Tagish Lake which seems unlikely to have originated in the outer main belt as previously suggested. In addition, we find that reducing the uncertainty of fireball velocity measurements below about 0.1 km/s does not lead to reduced uncertainties in the identification of their escape routes from the asteroid belt and, further, their ultimate source regions. The analysis suggests that camera networks should be optimized for the largest possible number of meteorite recoveries with measured speed precisions of order 0.1 km/s. We will present updated results based on a new NEO model (Granvik et al. 2018) and complement our data set with the falls that have been reported since 2016.

References:
Granvik, M. and Brown, P. (2018). "Identification of meteorite source regions in the Solar System", Icarus 311, 271-287.
Granvik, M., Morbidelli, A., Jedicke, R., Bolin, B., Bottke, W. F., Beshore, E., Vokrouhlicky, D., Delbo, M., Michel, P. (2016). "Super-catastrophic disruption of asteroids at small perihelion distances", Nature 530, 303-306.
Granvik, M., Morbidelli, A., Jedicke, R., Bolin, B., Bottke, W. F., Beshore, E., Vokrouhlicky, D., Nesvorny, D., Michel, P. (2018). "Debiased orbit and absolute-magnitude distributions for near-Earth objects", Icarus 312, 181-207.

How to cite: Granvik, M. and Brown, P.: Source regions for meteorite falls, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-774, https://doi.org/10.5194/epsc2020-774, 2020.

Cometary meteoroid trails exist in the vicinity of comets, forming fine structure of the interplanetary dust cloud. The trails consist predominantly of the largest cometary particles (with sizes of approximately 0.1 mm to 1 cm) which are ejected at low speeds and remain very close to the comet orbit for several revolutions around the Sun. In the 1970s two Helios spacecraft were launched towards the inner solar system. The spacecraft were equipped with in-situ dust sensors which measured the distribution of interplanetary dust in the inner solar system for the first time. 

When re-analysing the Helios data, Altobelli et al. (Astron. Astrophys., 448, 243-252, 2006) recognized a clustering of seven impacts, detected by Helios in a very narrow region of space at a true anomaly angle of 135 +/- 1 degrees, which the authors considered as potential cometary meteoroid trail particles. At the time, however, this hypothesis could not be studied further.

We re-analyse these candidate cometary trail particles in the Helios dust data to investigate the possibility that some or all of them indeed  originate from cometary trails and we constrain their source comets.

The Interplanetary Meteoroid Environment for eXploration (IMEX) dust streams in space model is a new universal model for cometary meteoroid streams in the inner solar system, developed by Soja et al. (Astron. Astrophys., 583, A18, 2015). We use IMEX to study cometary trail traverses by  Helios. 

During ten revolutions around the Sun, the Helios spacecraft intersected 13 cometary meteoroid trails. For the majority of these traverses the predicted dust fluxes are very low. In the narrow region of space where Helios detected the candidate dust particles, however, the spacecraft repeatedly traversed the trails of comets 45P/Honda-Mrkos-Pajdusakova and 72P/Denning-Fujikawa with relatively high predicted dust fluxes. 

The analysis of the detection times and particle impact directions shows that four detected particles are compatible with an origin from these two comets. By combining measurements and simulations we find a dust spatial density in these trails of approximately 10^-8 to 10^-7 m^-3.

The identification of potential cometary meteoroid trail particles in the Helios data greatly benefitted from the clustering of trail traverses in a rather narrow region of space. The in-situ detection and analysis of meteoroid trail particles which can be traced back to their source bodies by spacecraft-based dust analysers opens a new window to remote compositional analysis of comets and asteroids without the necessity to fly a spacecraft to or even land on those celestial bodies. This provides new science opportunities for future space missions like Destiny+, Europa Clipper and IMAP.

How to cite: Krüger, H., Strub, P., Sommer, M., Altobelli, N., Kimura, H., Lohse, A.-K., Grün, E., and Srama, R.: Helios spacecraft data revisited: Detection of cometary meteoroid trails by in-situ dust impacts, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-320, https://doi.org/10.5194/epsc2020-320, 2020.

The dynamical evolution of simulated meteoroid stream of the Quadrantids ejected from the parent body of the asteroid (196256) 2003 EH1 expects possible scenario for resonant motion. We found a peculiar behavior for this stream. Here, we show that the orbits of some ejected particles are strongly affected by the Lidov–Kozai mechanism that protects them from close encounters with Jupiter. Lack of close encounters with Jupiter leads to a rather smooth growth in the parameter MEGNO (Mean Exponential Growth factor of Nearby Orbits) and the behavior imply the stable motion of simulation particles of the Quadrantids meteoroid stream. A rather smooth path with nearly constant semi-major axis is obtained due to lack of close encounters with Jupiter. The coupled oscillation of the three orbital parameters, e, i, and ω, for stable ejected particles is observed.

However, close encounters with Jupiter are not treated by the Kozai formalism and can transfer particles away from the Kozai trajectories for unstable ejected particles over time. Other ejected particles have chaotic motion from simulations of the orbit of meteoroids are not affected by the Lidov – Kozai mechanism. We suppose that the reasons are the frequent close approaches of the ejected particles with Jupiter and they located near mean motion resonance 2:1J with Jupiter. The motion of these objects has considered to be chaotic in a long-time scale, and the close encounters with Jupiter are supposed to be the cause of the faster chaos. Another reason is that a non-resonant state near the mean motion resonance 2:1J has a strong influence on the motion of the Quadrantid meteor stream. This “weak chaos” is largely confined to the true anomaly. Consequently, the shape of the orbit can be computed reliably over much longer time scales than can the body’s position within the orbit. High value of the parameter MEGNO are due to frequent changes in semimajor axis induced by multiple close encounters with Jupiter near Hill sphere. We finally note that the chaotic behavior of the simulation particles of meteor stream may be caused not only by close encounter with planets but also by unstable mean motion or secular resonances.

We conjecture that the reasons of chaos are the overlap of stable secular resonances and unstable mean motions resonances and close and/or multiple close encounters with the major planets. The orbits of some ejected particles are strongly affected by the Lidov–Kozai mechanism that protects them from close encounters with Jupiter that leads to a rather smooth growth in the parameter MEGNO and the behavior imply the stable motion of simulation particles of the Quadrantids meteoroid stream.

The research was carried out within the state assignment of Ministry of Science and Higher Education of the Russian Federation (theme No. 0721-2020-0049)

References

Abedin, A., Spurný, P., Wiegert, P., Pokorný, P., Borovi cka, J., Brown, P., 2015. On the age and formation mechanism of the core of the Quadrantid meteoroid stream. Icarus 261, 100–117.

Cincotta, P.M., Girdano, C.M., Simo, C., 2003. Phase space structure of multi-dimensional systems by means of the mean exponential growth factor of nearby orbits. Phys. Nonlinear Phenom. 182 (3–4), 151–178.

Chirikov, B.V., 1979. A universal instability of many-dimensional oscillator systems. Phys. Rep. 52 (5), 263–379.

Galushina, T.Yu, Sambarov, G.E., 2017. The dynamical evolution and the force model for asteroid (196256) 2003 EH1. Planet. Space Sci. 142, 38.

Galushina, T.Yu, Sambarov, G.E., 2019. Dynamics of asteroid 3200 Phaethon under overlap of different resonances. Sol. Syst. Res. 53 (3), 215–223.

Gonczi, R., Rickman, H., Froeschle, C., 1992. The connection between Comet P/Machholz and the Quadrantid meteor. Mon. Not. Roy. Astron. Soc. 254, 627.

Hughes, D.W., Taylor, I.W., 1977. Observations of overdense Quadrantid radio meteors and the variation of the position of stream maxima with meteor magnitude. Mon. Not. Roy. Astron. Soc. 181, 517.

Kozai, Y., 1962. Secular perturbations of asteroids with high inclination and eccentricity. Astron. J. 67, 591–598.

Lidov, M.L., 1962. The evolution of orbits of artificial satellites of planets under the action of gravitational perturbations of external bodies. Planet. Space Sci. 9, 719.

Williams, I.P., Ryabova, G.O., Baturin, A.P., Chernitsov, A.M., 2004a. The parent of the Quadrantid meteoroid stream and asteroid 2003 EH1. Mon. Not. Roy. Astron. Soc. 355 (4), 1171–1181.

How to cite: Sambarov, G. E., Galushina, T. Yu., and Syusina, O. M.: How does the Lidov–Kozai mechanism protect Quadrantids meteoroid stream from close encounters with Jupiter?, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-551, https://doi.org/10.5194/epsc2020-551, 2020.

We describe a universal meteor simulation tool set named ASMODEUS and present several of its possible use cases. The toolset consists of a Monte-Carlo simulator of meteoroids entering the Earth’s atmosphere, functions for transformation to observer-centred coordinate frames representing virtual views of the sky, application of observational bias effects and a number of statistical tools for analyses of produced data sets and comparison to real-world data. The simulation has already been used in several areas of research, most notably estimates of meteoroid flux and de-biasing of real-world meteor observations and in investigation of how varying the initial properties of meteoroids affects the resulting meteors. It lends itself to many more possible applications, such as assessment of selection bias in ground-based observing systems, investigation of models of meteor flight and ablation, and evaluation of mass and population indices of meteor showers.

How to cite: Baláž, M., Tóth, J., Vereš, P., and Jedicke, R.: ASMODEUS Meteor Simulation Tool, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-341, https://doi.org/10.5194/epsc2020-341, 2020.

Mini-EUSO is a very wide (44°x44°) field of view telescope installed on August 2019 inside the Zvezda Module of the ISS, looking nadir through a UV transparent window and taking data since October 2019. Its optical system consists of two Fresnel lenses, focusing the light onto an array of 36 multi-anode photomultiplier tubes. The focal surface counts a total of 2304 pixels, each one having a footprint of about 6.5 km on ground. The instrument triggers on two different timescales, respectively 2.5 μs (D1) and 320 μs (D2), and perform a continuous monitoring of the UV emission at a 40.96 ms timescale (D3). At time of writing, about one thousand meteors on D3 data have been classified as meteors using our current detection algorithm. We describe here a concept of an alternative algorithm to recognize meteors in the D3 continuous data-stream, which can be also implemented in the future for online triggering, and show some examples of detected meteors by our instrument. We also performed a search of possible coincident detections of Mini-EUSO meteors by ground meteor and fireball networks, such as PRISMA in Italy, to gain a stereoscopic vision of the event itself. In light of these initial results, we present here the capabilities of Mini-EUSO instrument in meteor science.

How to cite: Barghini, D., Battisti, M., Belov, A., Bertaina, M. E., Bisconti, F., Capel, F., Casolino, M., Ebisuzaki, T., Gardiol, D., Klimov, P., Marcelli, L., Miyamoto, H., Picozza, P., Piotrowski, L. W., Prévot, G., Reali, E., Sakaki, N., and Takizawa, Y. and the Mini-EUSO collaboration: Meteor detection from space with Mini-EUSO telescope, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-800, https://doi.org/10.5194/epsc2020-800, 2020.

Near-Earth objects (NEOs) are typically fiercely monitored due to the inherent danger of their close encounters. Encounters with more massive objects at distances of a few lunar distances (LD) are relatively commonplace. However, fireball and meteor observation networks from around the world have witnessed ‘grazing’ events occur on several occasions [1, 2, 3, 4, 5]. Grazing events are characterized by their low impact angle and their possible re-entry into interplanetary space. These fireballs display how there are likely many smaller objects, that cannot be detected telescopically, that encounter the Earth all the time. Close encounters can quickly scatter meteoroids into drastically distinct orbits. This process is exemplified by the grazing fireball event detected by the Desert Fireball Network (DFN) in 2017 [5]. During this event, a ≥ 0.3 m object grazed the atmosphere coming from an Apollo-type orbit and exited with a JFC-like orbit. In order to characterize the population of objects in this small size range, we utilized the data collected by the Desert Fireball Network (DFN). The DFN is a continental-scale photographic fireball monitoring network covering over 2.5 million square kilometers of the Australian outback. The Earth’s close encounter flux in the 0.01-100 kg range was estimated using the impact flux observed by the DFN. To do this, several inherent biases had to be taken into account. Some of these biases include: limiting sensitivity of the fireball observatories, seasonal and diurnal variations in the flux, and gravitational focusing. These biases were all taken into consideration. The size-range analyzed in the DFN dataset was cutoff at small-sizes in order to remove the excess of fast, small meteoroids. Whereas, the diurnal and seasonal effects on the average flux of the DFN were considered negligible [6]. Most importantly, gravitational focusing must be corrected for or the flux of slower asteroidal material would be overestimated. The flux enhancement factor was accounted for using the global average enhancement determined by Opik [7], and scaled accordingly based on close encounter ¨ distance. In total, the close encounter population was modeled using 2.3 million test particles. The close encounter simulations, based on the DFN orbital dataset, demonstrated a significant population of close encounters at the centimeter/meter scale. Most of these bodies are negligibly affected during their close encounters; however, many experience considerable orbital changes (Fig. 1). Since the most likely objects to encounter the Earth are those with orbits more similar to the Earth, many close encounters come from asteroid-like (T_J > 3) objects. During the encounter, objects either gain or lose energy resulting in an inverse change to the objects T_J value. In total there appears to be a net gain of objects flung from asteroidal to JFC-like orbits. These encounters are considerably rare (about 0.16% of the total flux within 1.5 LD); however, considering the vast number of objects predicted to have close encounters at these small sizes, the size of this scattered population is not insignificant.

References: [1] Z Ceplecha. In: Bull. Astron. Inst. Czechoslov. 30 (1979), pp. 349–356. [2] J Borovicka and Z Ceplecha. In: A&A 257 (1992), pp. 323–328. [3] D. O. Revelle, R. W. Whitaker, and W. T. Armstrong. In: vol. 3116. 1997, pp. 156–167. [4] J.M. Madiedo et al. In: MNRAS 460.1 (2016), pp. 917–922. [5] Patrick M Shober et al. “Where Did They Come From, Where Did They Go: Grazing Fireballs”. In: The Astronomical Journal 159.5 (2020), p. 191. [6] I. Halliday and A.A. Griffin. In: Meteoritics 17.1 (1982), pp. 31–46. [7] E.J. Opik. ¨ In: Proc. R. Ir. Acad. 1951, pp. 165–199.

How to cite: Shober, P., Jansen-Sturgeon, T., Devillepoix, H., Sansom, E., Bland, P., Towner, M., Cupak, M., Howie, R., and Hartig, B.: Meteoroids Scattered by the Earth, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-884, https://doi.org/10.5194/epsc2020-884, 2020.

The visual observation of meteors has gathered over the last century the interest of scientists and the fascination of public. As the meteor observational techniques were spread worldwide, the meteor research community was avid of reliable mathematical relationships to derive further clues on these events: their orbital origin, their hazardous potential, etc. For such purpose, the combination of the meteor event observed flight parameters seemed to comply with these goals. Moreover, this approach was little by little more feasible given the technology growth that gradually improved the accuracy of the observations. Amongst all the suggested flight parameter relationships available in the literature, the one introduced by Ceplecha and McCrosky [1] in the mid 70’s became timely and used in many studies as a ‘ground truth’. The empirical work of Ceplecha and McCrosky [1] was also mathematically supported by the single body Newtonian formulation that is still widely used to model the meteor trajectory [2]. Moreover, Ceplecha and McCrosky [1] expanded the use of this relationship to elaborate a meteor classification. The classification relies on the value of a criterion, called PE, which ranks the value of the correlation given by the parameters included in the relationship. Although the authors did not expect the classification to be extremely accurate, it allows quick interpretation of the event under study. Consequently, both the criterion and the classification have played a relevant role in the scientific publication over the decades.

The PE criterion relates the meteor observed end height to its atmospheric entry velocity, mass and flight trajectory angle. To keep the PE calculation straightforward, and because it originally was used for decelerating events, the influence of the meteoroid mass loss (ablation and shape coefficient) was simplified and a mean value for all the meteors registered in the same database was assumed. In the recent years, several alternative formulations for the meteoroid atmospheric flight modelling have been proposed in order to reduce the required analysis-related assumptions and consequent results’ inaccuracies. Amongst them, a formulation based on scaling laws and dimensionless variables has obtained significant results when tackling with different common meteor related studies (see e.g. [3-9]). The main advantage of this methodology is that it provides relevant clues on the event under study removing the necessity of stating initial assumptions on the meteor parameters. Additionally, the accuracy of the outcome is, in most cases, directly linked to the quality of the observations. Interestingly, this new methodology quantifies the meteoroid mass loss in a unique and straightforward way by matching the meteor trajectory (observed height and velocity values) with two dimensionless parameters that are physically meaningful. On one hand, the ballistic coefficient, α, expresses the drag intensity suffered by the meteor body during its flight and it is proportional to the mass of the atmospheric column with the initial meteoroid cross section area along the trajectory divided by the meteoroid’s pre-atmospheric mass. On the other, the mass loss parameter, β, characterizes the mass loss rate of the meteoroid; it can be expressed as the fraction of the kinetic energy per mass unit of the body that is transferred to the body in the form of heat divided by the effective destruction enthalpy.

Since these two parameters comprise all the meteoroid flight variables earlier included in the PE criterion, but avoid artificial assumptions, we have proposed and studied the hypothesis that there should exist a mathematical expression involving  these two parameters which offers an improved classification criterion [2]. In this work, we verify this hypothesis. The results of our study show that: i) under the same original assumptions [1] the derived log(2αβ) which we advocate using leads to the exactly same PE formula obtained by Ceplecha and McCrosky [1]; ii) the newly offered possibility to include the individual event mass-loss effects in the criterion allows an accurate formulation that still remains simple to implement; iii) the improved criterion is scalable – it is suitable for expanding the classification beyond fully disintegrating fireballs to larger impactors, including meteorite-dropping fireballs. We use the Prairie Network meteor observations for comparative analysis, which demonstrates the effectiveness and reliability of the new formulation.

References

[1] Ceplecha Z., McCrosky R. E.,1976, JGR, 81, 6257. https://doi.org/10.1029/JB081i035p06257

[2] Moreno-Ibáñez M., Gritsevich M., Trigo-Rodriguez J. M., Silber E. A., 2020, MNRAS, 494 (1), 316. https://doi.org/10.1093/mnras/staa646

[3] Gritsevich M. I., 2009, Adv. Space Res., 44(3), 323.  http://dx.doi.org/10.1016/j.asr.2009.03.030

[4] Gritsevich M. I., Stulov V. P., Turchak L. I.,2012, Cosmic Res., 50(1), 56. http://dx.doi.org/10.1134/S0010952512010017

[5] Bouquet A., Baratoux D., Vaubaillon J., et al. 2014, PSS, 103, 238. http://dx.doi.org/10.1016/j.pss.2014.09.001

[6] Moreno-Ibáñez M., Gritsevich M., Trigo-Rodríguez J. M., 2015, Icarus, 250, 544. http://dx.doi.org/10.1016/j.icarus.2014.12.027

[7] Trigo-Rodríguez J. M., Lyytinen E., Gritsevich M., et al., 2015, MNRAS,449 (2), 2119. http://dx.doi.org/10.1093/mnras/stv378

[8] Lyytinen E., Gritsevich M., 2016, PSS, 120, 35. http://dx.doi.org/10.1016/j.pss.2015.10.012

[9] Sansom E. K., Gritsevich M., Devillepoix H. A. R., et al., 2019, ApJ, 885 (2). https://doi.org/10.3847/1538-4357/ab4516 

How to cite: Moreno-Ibáñez, M., Gritsevich, M., Trigo-Rodríguez, J. M., Silber, E. A., and Peltoniemi, J.: Classification of fireballs: upgrading the PE criterion, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-64, https://doi.org/10.5194/epsc2020-64, 2020.

1. Introduction

When a meteoroid enters the atmosphere, it experiences aerodynamic drag and dynamic pressure. Shock waves can be generated by the hypersonic flight in the atmosphere, fragmentation/airburst and impact in the ground. The hypersonic projectile motion in the atmosphere causes the formation of a Mach cone [1-3]. The shock waves generated during this hypersonic entry propagate almost perpendicular to the trajectory. Fragmentation of the meteoroid creates shock waves that propagate omnidirectionally [1,2]. In large impact events, bolides and/or crater events, the first wave to arrive at the seismic station is the P wave generated directly under the terminal point of the trajectory [4]. After the P wave, air-coupled Rayleigh wave arrive. Airwaves generated by the Mach cone will arrive later as they travel at the speed of sound [1]. The airwave that originates from the point of the trajectory having the shortest distance to the seismic station arrives first and they show the strongest seismic signals in time series data [1,4]. In fireball events, airwaves are a dominant seismic signature [1,4].

2. Aim and Methodology

In this study, we searched for seismic signals from fireballs that have been observed by the Desert Fireball Network (DFN), over a 6-year observational period (2014-2019). The DFN is the world’s largest fireball camera network, located in the Australian outback and consisting of 52 observatories, covering an area of 3 million km² aimed to detect fireballs, recover meteorites and to calculate their orbits [5,6]. We used processed trajectory data from the DFN [6], with seismic data acquired from the Australian National Seismograph Network (ANSN).

The criteria that determined if a seismic signal in time series data could be confidently classified as a signal coming from a fireball event were that the amplitude of the signals must be similar or lower than previously confirmed seismic signals from fireballs, the signal must be within the calculated arrival times of the airwave (direct or ground-coupled Rayleigh wave), there must not be any earthquake activity at the same time, and there must not be any clear anthropogenic-related noise.

We checked if a seismic station could encounter the planar wavefront from the Mach cone. If the shortest distance to the seismic station is perpendicular to the bright flight trajectory and arrival times for the airwaves fit, signals are classified as originating from the Mach cone. If the shortest distance is not perpendicular to the bright flight trajectory, any seismic signals (if they fit with calculated arrival times), are assumed to come from an omnidirectional source that could be caused by fragmentation along the trajectory.

3. Results

Weak and short seismic signals were found for 24 fireball events out of 995 surveyed within 200 km of a seismic station (corresponding to 2.4%). The observed seismic signals in our dataset correspond to airwaves (either as direct airwaves or ground-coupled Rayleigh waves). We found 13 fireballs for which we suspect the signals to have originated from the Mach cone traverse and for 11 fireballs we detected signals that might originate from an airburst. No surveyed fireballs were detected by more than one seismic station. The total of 18 out of 24 signals showed the highest peak in vertical component. The shortest distance between the bright flight trajectory to the seismic station is about 50 km. Fireballs for which seismic signals have been detected cover the complete range of impact angles.

4. Discussion and Conclusion

The weak and short signals that we see in our data are likely direct airwaves, or ground-coupled Rayleigh waves generated by fireball events. In many cases it is not possible to distinguish whether the signal originated from the direct airwave or ground-coupled Rayleigh wave due to overlapping arrival time windows and background noise. The reason why we see signals of some fireballs and not others is probably due to distance, directionality, noise, wind and properties of the seismic station.

We report possible detections of seismic signatures originating from 2.4% of surveyed fireballs observed by the DFN. Unlike other studies who used data from images, seismic stations and infrasound to calculate the orbit and energies of meteors, this study uses information about the trajectory and timing of fireballs observed by the DFN to search for seismic signals.

The importance of this work is evident as these impact events occur on a daily basis, yet are rarely reported as seismic events because their impact energy is often not sufficient to cause quakes that are detectable by seismic stations. Furthermore, understanding frequent meteoroid encounters on Earth could help us make better predictions about what may be impacting Earth and other planetary bodies, such as Mars, in terms of small impact events.

References

[1] Edwards W. N. et al. (2008) Rev. Geophys., 46(4).

[2] Tancredi G. et al. (2009) Meteoritics & Planet. Sci., 44, 1967-1984.

[3] Tauzin B. et al. (2013) Geophys. Res., 40(14), 3522-3526.

[4] Brown P. G. et al. (2003) Meteoritics & Planet. Sci., 38, 989-1003.

[5] Devillepoix H. A. R. et al. (2018) Meteoritics & Planet. Sci., 53(10), 2212-2227.

[6]  Devillepoix H. A. R. et al. (2019) MNRAS, 483(4), 5166-5178.

How to cite: Neidhart, T., Miljković, K., Sansom, E. K., Devillepoix, H. A. R., Kawamura, T., Dimech, J., Wieczorek, M., and Bland, P. A.: Suspected seismic signals from DFN fireballs, Europlanet Science Congress 2020, online, 21 Sep–9 Oct 2020, EPSC2020-638, https://doi.org/10.5194/epsc2020-638, 2020.

Main text

The UK has a long history of meteorite falls (where the meteorite fireball is witnessed, and the stone recovered, dating back to 1623 (MetBull, 2020). But the last meteorite fall in the UK was nearly 30 years ago when the Glatton stone, an L6 ordinary chondrite was recovered in 1991 (Hutchinson et al., 1991). Meteorite falls are important samples as they are usually recovered within days of the fireball event.  As such, they have not experienced the deleterious effects terrestrial weathering that can change their extraterrestrial mineralogy, chemistry and isotopic composition (Bland et al., 2006). In exceptional circumstances rapidly recovered falls may have avoided rainfall so that soluble extraterrestrial minerals such as salts may be preserved (Chan et al., 2018). Therefore, meteorite falls represent much more pristine extraterrestrial material than their find counterparts within the same group, and consequently, characterisation of their texture and chemical signatures provides a clearer window into solar system processes. However, even falls are limited in their interpretive power as the geological context of the stone (i.e. where in the solar system it originated from) is unknown.

To derive this contextual information requires the imaging of the fireball event from multiple geographical positions (Devillepoix et al., 2020). This data provides two vital pieces of information; the initial orbit of the meteoroid can be calculated and the final fall position can be predicted with increased accuracy (Devillepoix et al., 2020). As such, dedicated fireball camera networks (Bowden, 2006) are entering ‘a golden age’ with improvements in hardware and software capabilities as well as a reduction in production costs (Spurný et al., 2014). Continent-scale observatories have been established (Howie et al., 2017) and global networks are under construction (Devillepoix et al., 2020). In addition, these same developments have enabled the amateur astronomy community to construct their own networks either as groups or individuals with functional data pipelines and observations that can rival funded networks. Consequently, the number of recovered meteorite falls with orbits globally has grown rapidly over recent years (Borovička et al., 2015).

The UK is a hotbed for such activity but does not have a recent recovered meteorite fall…yet. There are currently two active academically funded networks in the UK: the UK Fireball Network (UKFN: part of the Global Fireball Observatory built on the hardware and software developed by the Desert Fireball Network in Australia), and the System for Capture of Asteroid and Meteorite Paths (SCAMP; the UK arm of the French-led Fireball Recovery and InterPlanetary Observation Network (FRIPON)) (Figure 1, 2). In addition, there are two major amateur networks the UK Meteor Observation Network (UKMON), and NEMETODE, as well as an emerging presence of the Raspberry-Pi based Global Meteor Network and countless individual citizen scientists also imaging the UK night skies (Figure 1, 2).

However, environmental factors in the UK such as light pollution and regular low-lying cloud cover means that a single fireball may not be captured multiple times by one network, preventing them from calculating an orbit and accurate fall position. As such, these networks, together with UK planetary scientists and National museums, have formed a collaborative data-sharing initiative called the UK Fireball Alliance (UKFAll) in order to maximise the chances of capturing a meteorite-dropping fireball event that makes landfall on the UK.

Since the initiation of UKFAll in late 2018 many joint fireball observations have been made between UK networks including a fireball on the 16^th February 2020 that likely dropped a few 10s of grams of extraterrestrial material into the North Sea (Figure 3).

However, the diversity of hardware, software and data processing pipelines for capturing fireball events vary between camera networks. This hinders the speed of detecting and triangulating a meteorite-dropping fireball event as each fireball requires a bespoke solution to translate the data into the other networks’ format. A consistent method for rapidly transferring and converting the diversity of outputs produced by each network into a standard format that can be read and utilised by each network is required and is critical to facilitate a rapid UK response to fireball events and associated recovery effort.

Here we describe the first iteration of a new code that will enable rapid conversion of data outputs from both video and still image camera networks. The code provides an effective bridging solution, while the ultimate aim is to agree and implement a globally accepted standardised format for fireball observations that can be readily transferred and utilised between camera networks to facilitate meteorite fall recovery.

We also describe the logistical issues encountered by UKFAll and the solutions being implemented, including: recruitment of citizen scientists as searchers; conduct and liability issues; and best practice for collection.